Overview

Dataset statistics

Number of variables20
Number of observations4250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory664.2 KiB
Average record size in memory160.0 B

Variable types

NUM15
BOOL3
CAT2

Warnings

state has a high cardinality: 51 distinct values High cardinality
total_day_charge is highly correlated with total_day_minutesHigh correlation
total_day_minutes is highly correlated with total_day_chargeHigh correlation
total_eve_charge is highly correlated with total_eve_minutesHigh correlation
total_eve_minutes is highly correlated with total_eve_chargeHigh correlation
total_night_charge is highly correlated with total_night_minutesHigh correlation
total_night_minutes is highly correlated with total_night_chargeHigh correlation
total_intl_charge is highly correlated with total_intl_minutesHigh correlation
total_intl_minutes is highly correlated with total_intl_chargeHigh correlation
number_vmail_messages has 3139 (73.9%) zeros Zeros
number_customer_service_calls has 886 (20.8%) zeros Zeros

Reproduction

Analysis started2022-06-20 20:12:54.346319
Analysis finished2022-06-20 20:13:10.040968
Duration15.69 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

state
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
WV
 
139
MN
 
108
ID
 
106
AL
 
101
VA
 
100
Other values (46)
3696 
ValueCountFrequency (%) 
WV1393.3%
 
MN1082.5%
 
ID1062.5%
 
AL1012.4%
 
VA1002.4%
 
OR992.3%
 
TX982.3%
 
UT972.3%
 
NJ962.3%
 
NY962.3%
 
Other values (41)321075.5%
 
2022-06-21T01:43:10.113054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-06-21T01:43:10.202377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

account_length
Real number (ℝ≥0)

Distinct215
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2362353
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Memory size33.2 KiB
2022-06-21T01:43:10.264541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.45
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.69840057
Coefficient of variation (CV)0.3960483996
Kurtosis-0.1321747749
Mean100.2362353
Median Absolute Deviation (MAD)27
Skewness0.1223273244
Sum426004
Variance1575.963008
MonotocityNot monotonic
2022-06-21T01:43:10.330888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
90531.2%
 
87511.2%
 
93501.2%
 
105481.1%
 
120481.1%
 
100481.1%
 
98471.1%
 
127471.1%
 
116471.1%
 
112461.1%
 
Other values (205)376588.6%
 
ValueCountFrequency (%) 
170.2%
 
22< 0.1%
 
370.2%
 
42< 0.1%
 
52< 0.1%
 
ValueCountFrequency (%) 
2431< 0.1%
 
2322< 0.1%
 
2252< 0.1%
 
2242< 0.1%
 
2222< 0.1%
 

area_code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
area_code_415
2108 
area_code_408
1086 
area_code_510
1056 
ValueCountFrequency (%) 
area_code_415210849.6%
 
area_code_408108625.6%
 
area_code_510105624.8%
 
2022-06-21T01:43:10.388696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-06-21T01:43:10.423451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:10.469660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length13
Min length13
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
no
3854 
yes
396 
ValueCountFrequency (%) 
no385490.7%
 
yes3969.3%
 
2022-06-21T01:43:10.502586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
no
3138 
yes
1112 
ValueCountFrequency (%) 
no313873.8%
 
yes111226.2%
 
2022-06-21T01:43:10.525292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

number_vmail_messages
Real number (ℝ≥0)

ZEROS

Distinct46
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.631764706
Minimum0
Maximum52
Zeros3139
Zeros (%)73.9%
Memory size33.2 KiB
2022-06-21T01:43:10.569260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile36
Maximum52
Range52
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.4398822
Coefficient of variation (CV)1.761045147
Kurtosis0.2730383375
Mean7.631764706
Median Absolute Deviation (MAD)0
Skewness1.373091038
Sum32435
Variance180.6304335
MonotocityNot monotonic
2022-06-21T01:43:10.639024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
0313973.9%
 
31691.6%
 
28581.4%
 
24571.3%
 
29571.3%
 
33551.3%
 
27541.3%
 
26531.2%
 
30471.1%
 
32471.1%
 
Other values (36)61414.4%
 
ValueCountFrequency (%) 
0313973.9%
 
41< 0.1%
 
62< 0.1%
 
82< 0.1%
 
1040.1%
 
ValueCountFrequency (%) 
521< 0.1%
 
502< 0.1%
 
4930.1%
 
4840.1%
 
4740.1%
 

total_day_minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2596
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:10.712751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.59
Q1143.325
median180.45
Q3216.2
95-th percentile271.055
Maximum351.5
Range351.5
Interquartile range (IQR)72.875

Descriptive statistics

Standard deviation54.01237333
Coefficient of variation (CV)0.2996365982
Kurtosis-0.05670971637
Mean180.2596
Median Absolute Deviation (MAD)36.6
Skewness-0.006910229801
Sum766103.3
Variance2917.336473
MonotocityNot monotonic
2022-06-21T01:43:10.786110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
189.3100.2%
 
18090.2%
 
177.180.2%
 
15480.2%
 
184.580.2%
 
18570.2%
 
19770.2%
 
157.170.2%
 
217.270.2%
 
230.770.2%
 
Other values (1833)417298.2%
 
ValueCountFrequency (%) 
02< 0.1%
 
2.61< 0.1%
 
6.61< 0.1%
 
7.21< 0.1%
 
7.81< 0.1%
 
ValueCountFrequency (%) 
351.51< 0.1%
 
346.81< 0.1%
 
345.31< 0.1%
 
338.41< 0.1%
 
337.41< 0.1%
 

total_day_calls
Real number (ℝ≥0)

Distinct120
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.90729412
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:10.847927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.85081731
Coefficient of variation (CV)0.1986923726
Kurtosis0.1935936484
Mean99.90729412
Median Absolute Deviation (MAD)13
Skewness-0.08581246337
Sum424606
Variance394.054948
MonotocityNot monotonic
2022-06-21T01:43:10.912483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1051012.4%
 
95972.3%
 
110922.2%
 
94922.2%
 
112902.1%
 
102892.1%
 
97882.1%
 
107872.0%
 
100852.0%
 
101842.0%
 
Other values (110)334578.7%
 
ValueCountFrequency (%) 
02< 0.1%
 
301< 0.1%
 
341< 0.1%
 
351< 0.1%
 
361< 0.1%
 
ValueCountFrequency (%) 
1651< 0.1%
 
1602< 0.1%
 
1582< 0.1%
 
1572< 0.1%
 
15630.1%
 

total_day_charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.64468235
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:10.975466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.5735
Q124.365
median30.68
Q336.75
95-th percentile46.081
Maximum59.76
Range59.76
Interquartile range (IQR)12.385

Descriptive statistics

Standard deviation9.182096033
Coefficient of variation (CV)0.2996309744
Kurtosis-0.0565844345
Mean30.64468235
Median Absolute Deviation (MAD)6.225
Skewness-0.006912526228
Sum130239.9
Variance84.31088755
MonotocityNot monotonic
2022-06-21T01:43:11.036960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32.18100.2%
 
30.690.2%
 
30.1180.2%
 
26.1880.2%
 
31.3780.2%
 
30.570.2%
 
28.6670.2%
 
28.6370.2%
 
23.5870.2%
 
39.2270.2%
 
Other values (1833)417298.2%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.441< 0.1%
 
1.121< 0.1%
 
1.221< 0.1%
 
1.331< 0.1%
 
ValueCountFrequency (%) 
59.761< 0.1%
 
58.961< 0.1%
 
58.71< 0.1%
 
57.531< 0.1%
 
57.361< 0.1%
 

total_eve_minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1773
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.1739059
Minimum0
Maximum359.3
Zeros1
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:11.098555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.2
Q1165.925
median200.7
Q3233.775
95-th percentile282.71
Maximum359.3
Range359.3
Interquartile range (IQR)67.85

Descriptive statistics

Standard deviation50.24951818
Coefficient of variation (CV)0.2510293135
Kurtosis0.04345320215
Mean200.1739059
Median Absolute Deviation (MAD)33.7
Skewness-0.03041458624
Sum850739.1
Variance2525.014078
MonotocityNot monotonic
2022-06-21T01:43:11.169991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
230.9100.2%
 
19490.2%
 
169.990.2%
 
199.790.2%
 
187.590.2%
 
19680.2%
 
221.180.2%
 
216.580.2%
 
211.580.2%
 
209.480.2%
 
Other values (1763)416498.0%
 
ValueCountFrequency (%) 
01< 0.1%
 
22.31< 0.1%
 
37.81< 0.1%
 
41.71< 0.1%
 
42.21< 0.1%
 
ValueCountFrequency (%) 
359.31< 0.1%
 
352.11< 0.1%
 
351.61< 0.1%
 
349.41< 0.1%
 
348.51< 0.1%
 

total_eve_calls
Real number (ℝ≥0)

Distinct123
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1764706
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:11.235970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.9085911
Coefficient of variation (CV)0.1987352019
Kurtosis0.1145997215
Mean100.1764706
Median Absolute Deviation (MAD)13
Skewness-0.02081182363
Sum425750
Variance396.3519998
MonotocityNot monotonic
2022-06-21T01:43:11.306822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105982.3%
 
103962.3%
 
91952.2%
 
97912.1%
 
108882.1%
 
94882.1%
 
96882.1%
 
88872.0%
 
101862.0%
 
104852.0%
 
Other values (113)334878.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
121< 0.1%
 
361< 0.1%
 
381< 0.1%
 
431< 0.1%
 
ValueCountFrequency (%) 
1701< 0.1%
 
1691< 0.1%
 
1681< 0.1%
 
1591< 0.1%
 
1571< 0.1%
 

total_eve_charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1572
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.01501176
Minimum0
Maximum30.54
Zeros1
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:11.372826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.05
Q114.1025
median17.06
Q319.8675
95-th percentile24.031
Maximum30.54
Range30.54
Interquartile range (IQR)5.765

Descriptive statistics

Standard deviation4.271211992
Coefficient of variation (CV)0.2510260969
Kurtosis0.04332949445
Mean17.01501176
Median Absolute Deviation (MAD)2.86
Skewness-0.03038789084
Sum72313.8
Variance18.24325188
MonotocityNot monotonic
2022-06-21T01:43:11.439160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14.25130.3%
 
18.79130.3%
 
16.12130.3%
 
16.97120.3%
 
15.9120.3%
 
18.96110.3%
 
17.09100.2%
 
16.8100.2%
 
19.63100.2%
 
17.690.2%
 
Other values (1562)413797.3%
 
ValueCountFrequency (%) 
01< 0.1%
 
1.91< 0.1%
 
3.211< 0.1%
 
3.541< 0.1%
 
3.591< 0.1%
 
ValueCountFrequency (%) 
30.541< 0.1%
 
29.931< 0.1%
 
29.891< 0.1%
 
29.71< 0.1%
 
29.621< 0.1%
 

total_night_minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1757
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.5278824
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:11.690845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.09
Q1167.225
median200.45
Q3234.7
95-th percentile282.71
Maximum395
Range395
Interquartile range (IQR)67.475

Descriptive statistics

Standard deviation50.35354807
Coefficient of variation (CV)0.251104971
Kurtosis0.1148535776
Mean200.5278824
Median Absolute Deviation (MAD)33.55
Skewness0.008490819348
Sum852243.5
Variance2535.479804
MonotocityNot monotonic
2022-06-21T01:43:11.759593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
186.2110.3%
 
208.9100.2%
 
21480.2%
 
214.680.2%
 
221.680.2%
 
169.480.2%
 
230.180.2%
 
194.380.2%
 
221.780.2%
 
214.780.2%
 
Other values (1747)416598.0%
 
ValueCountFrequency (%) 
01< 0.1%
 
23.21< 0.1%
 
43.71< 0.1%
 
451< 0.1%
 
46.71< 0.1%
 
ValueCountFrequency (%) 
3951< 0.1%
 
381.91< 0.1%
 
381.61< 0.1%
 
377.51< 0.1%
 
367.71< 0.1%
 

total_night_calls
Real number (ℝ≥0)

Distinct128
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.83952941
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:11.825160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q186
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.09321979
Coefficient of variation (CV)0.2012551532
Kurtosis0.07721835856
Mean99.83952941
Median Absolute Deviation (MAD)14
Skewness0.005273110227
Sum424318
Variance403.7374815
MonotocityNot monotonic
2022-06-21T01:43:11.894485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1051002.4%
 
99922.2%
 
95912.1%
 
102902.1%
 
91882.1%
 
94882.1%
 
104872.0%
 
98872.0%
 
100862.0%
 
103852.0%
 
Other values (118)335679.0%
 
ValueCountFrequency (%) 
01< 0.1%
 
331< 0.1%
 
361< 0.1%
 
382< 0.1%
 
401< 0.1%
 
ValueCountFrequency (%) 
1751< 0.1%
 
1701< 0.1%
 
1651< 0.1%
 
1641< 0.1%
 
1611< 0.1%
 

total_night_charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct992
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.023891765
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Memory size33.2 KiB
2022-06-21T01:43:11.958098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.3145
Q17.5225
median9.02
Q310.56
95-th percentile12.7255
Maximum17.77
Range17.77
Interquartile range (IQR)3.0375

Descriptive statistics

Standard deviation2.265921811
Coefficient of variation (CV)0.2511025033
Kurtosis0.1148651735
Mean9.023891765
Median Absolute Deviation (MAD)1.51
Skewness0.008444754041
Sum38351.54
Variance5.134401655
MonotocityNot monotonic
2022-06-21T01:43:12.022629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.4180.4%
 
10.8170.4%
 
9.63170.4%
 
8.15170.4%
 
9.66160.4%
 
8.82150.4%
 
10.49150.4%
 
9.76150.4%
 
9.09140.3%
 
7.69140.3%
 
Other values (982)409296.3%
 
ValueCountFrequency (%) 
01< 0.1%
 
1.041< 0.1%
 
1.971< 0.1%
 
2.031< 0.1%
 
2.11< 0.1%
 
ValueCountFrequency (%) 
17.771< 0.1%
 
17.191< 0.1%
 
17.171< 0.1%
 
16.991< 0.1%
 
16.551< 0.1%
 

total_intl_minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.25607059
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Memory size33.2 KiB
2022-06-21T01:43:12.087147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.6
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.760101726
Coefficient of variation (CV)0.2691188309
Kurtosis0.7029511928
Mean10.25607059
Median Absolute Deviation (MAD)1.8
Skewness-0.2413595394
Sum43588.3
Variance7.618161539
MonotocityNot monotonic
2022-06-21T01:43:12.153836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.1751.8%
 
11.4731.7%
 
9.8731.7%
 
10.2721.7%
 
10.9711.7%
 
11.3701.6%
 
10.1691.6%
 
9.7681.6%
 
9.5661.6%
 
10.5661.6%
 
Other values (158)354783.5%
 
ValueCountFrequency (%) 
0220.5%
 
0.41< 0.1%
 
1.12< 0.1%
 
1.31< 0.1%
 
22< 0.1%
 
ValueCountFrequency (%) 
201< 0.1%
 
19.72< 0.1%
 
19.31< 0.1%
 
19.21< 0.1%
 
18.91< 0.1%
 

total_intl_calls
Real number (ℝ≥0)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.426352941
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Memory size33.2 KiB
2022-06-21T01:43:12.211549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.463069113
Coefficient of variation (CV)0.5564556522
Kurtosis3.263227525
Mean4.426352941
Median Absolute Deviation (MAD)1
Skewness1.360122209
Sum18812
Variance6.066709454
MonotocityNot monotonic
2022-06-21T01:43:12.268786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
384719.9%
 
479518.7%
 
264415.2%
 
559814.1%
 
64089.6%
 
72726.4%
 
12265.3%
 
81533.6%
 
91263.0%
 
10591.4%
 
Other values (11)1222.9%
 
ValueCountFrequency (%) 
0220.5%
 
12265.3%
 
264415.2%
 
384719.9%
 
479518.7%
 
ValueCountFrequency (%) 
201< 0.1%
 
191< 0.1%
 
1840.1%
 
171< 0.1%
 
1670.2%
 

total_intl_charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.769654118
Minimum0
Maximum5.4
Zeros22
Zeros (%)0.5%
Memory size33.2 KiB
2022-06-21T01:43:12.329567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.94
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.7452041364
Coefficient of variation (CV)0.2690603609
Kurtosis0.7033212689
Mean2.769654118
Median Absolute Deviation (MAD)0.48
Skewness-0.2416706661
Sum11771.03
Variance0.5553292049
MonotocityNot monotonic
2022-06-21T01:43:12.395425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3751.8%
 
3.08731.7%
 
2.65731.7%
 
2.75721.7%
 
2.94711.7%
 
3.05701.6%
 
2.73691.6%
 
2.62681.6%
 
2.84661.6%
 
2.57661.6%
 
Other values (158)354783.5%
 
ValueCountFrequency (%) 
0220.5%
 
0.111< 0.1%
 
0.32< 0.1%
 
0.351< 0.1%
 
0.542< 0.1%
 
ValueCountFrequency (%) 
5.41< 0.1%
 
5.322< 0.1%
 
5.211< 0.1%
 
5.181< 0.1%
 
5.11< 0.1%
 

number_customer_service_calls
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.559058824
Minimum0
Maximum9
Zeros886
Zeros (%)20.8%
Memory size33.2 KiB
2022-06-21T01:43:12.450364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.31143353
Coefficient of variation (CV)0.8411700126
Kurtosis1.655618759
Mean1.559058824
Median Absolute Deviation (MAD)1
Skewness1.082691586
Sum6626
Variance1.719857904
MonotocityNot monotonic
2022-06-21T01:43:12.497146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1152435.9%
 
294722.3%
 
088620.8%
 
355813.1%
 
42094.9%
 
5811.9%
 
6280.7%
 
7130.3%
 
82< 0.1%
 
92< 0.1%
 
ValueCountFrequency (%) 
088620.8%
 
1152435.9%
 
294722.3%
 
355813.1%
 
42094.9%
 
ValueCountFrequency (%) 
92< 0.1%
 
82< 0.1%
 
7130.3%
 
6280.7%
 
5811.9%
 

churn
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
no
3652 
yes
598 
ValueCountFrequency (%) 
no365285.9%
 
yes59814.1%
 
2022-06-21T01:43:12.529547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2022-06-21T01:42:57.663065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:57.734117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:57.783287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:57.834146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:57.883443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:57.932743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:57.982991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.033068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.082517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.133751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.183481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.233759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.285033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.336111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.383616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.430852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.478862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.526918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.652994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.702728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.752083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.802251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.852192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.900971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:58.956977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.008339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.059325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.110816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.162235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.210144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.258317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.308893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.359660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.412261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.464163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.516086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.568715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.621155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.672593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.726515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.779305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.832368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.886695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.940479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:42:59.991202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.041231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.090083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.139462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.190793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.240968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.360765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.412378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.463609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.513640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.565877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.616976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.668284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.720640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.772728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.821621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.870096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.919154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:00.968443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.019710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.069803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.119847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.171315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.223017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.272990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.325466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.376830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.428283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.482576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.535325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.584321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.632891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.683361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.733976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.786608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.838032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.889526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.941882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:01.994505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.046014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.099667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.152854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.206077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.260481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.315880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.366264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.497102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.549508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.600382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.653137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.704543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.756156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.808625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.860766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.911909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:02.965685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.018257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.071060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.124747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.178344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.228822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.278728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.327746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.376910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.428299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.478798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.528720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.579811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.630979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.680713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.732969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.784375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.835855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.888612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.941080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:03.989957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:04.038374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:04.090524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:04.196552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:04.303116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:04.520595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:04.628980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:04.740102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:04.843358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:05.413294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.465663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.518938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.572666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:05.777745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.829203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.882692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.934625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:05.986860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.039887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.092791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.144836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.199039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.252603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.306164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.360391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.416130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.467084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.517560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.569942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.622824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.677126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.730252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.784499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.838937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.893186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:06.946653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.002038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.056761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.111613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.167186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.222758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.275518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.327063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.379070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.431699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.486081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.539757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.593414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.649367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.703661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.756900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.813048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.866900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.921960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:07.977771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.033498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.085802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.137531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.185249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.233486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:08.455669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.505834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.556435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:08.606745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-21T01:43:09.052830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.099872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.148871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.196876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.245164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.294367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.343130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.390838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.440969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.489946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.539105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.589282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.639479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.686384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-06-21T01:43:12.575740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-21T01:43:12.677918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-21T01:43:12.776784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-21T01:43:12.883319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-06-21T01:43:12.971969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-06-21T01:43:09.840791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-21T01:43:09.976847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

stateaccount_lengtharea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
0OH107area_code_415noyes26161.612327.47195.510316.62254.410311.4513.733.701no
1NJ137area_code_415nono0243.411441.38121.211010.30162.61047.3212.253.290no
2OH84area_code_408yesno0299.47150.9061.9885.26196.9898.866.671.782no
3OK75area_code_415yesno0166.711328.34148.312212.61186.91218.4110.132.733no
4MA121area_code_510noyes24218.28837.09348.510829.62212.61189.577.572.033no
5MO147area_code_415yesno0157.07926.69103.1948.76211.8969.537.161.920no
6LA117area_code_408nono0184.59731.37351.68029.89215.8909.718.742.351no
7WV141area_code_415yesyes37258.68443.96222.011118.87326.49714.6911.253.020no
8IN65area_code_415nono0129.113721.95228.58319.42208.81119.4012.763.434yes
9RI74area_code_415nono0187.712731.91163.414813.89196.0948.829.152.460no

Last rows

stateaccount_lengtharea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
4240AR127area_code_415noyes27157.610726.79280.64923.8575.1773.388.042.161no
4241WA80area_code_510nono0157.010126.69208.812717.75113.31095.1016.224.372no
4242MN150area_code_408nono0170.011528.90162.713813.83267.27712.028.322.240no
4243ND140area_code_510nono0244.711541.60258.610121.98231.311210.417.562.031yes
4244AZ97area_code_510nono0252.68942.94340.39128.93256.56711.548.852.381yes
4245MT83area_code_415nono0188.37032.01243.88820.72213.7799.6210.362.780no
4246WV73area_code_408nono0177.98930.24131.28211.15186.2898.3811.563.113no
4247NC75area_code_408nono0170.710129.02193.112616.41129.11045.816.971.861no
4248HI50area_code_408noyes40235.712740.07223.012618.96297.511613.399.952.672no
4249VT86area_code_415noyes34129.410222.00267.110422.70154.81006.979.3162.510no